Detecting anomalous events in a discriminator of an embedded device

ABSTRACT

In an embodiment, an apparatus includes: a sensor to sense real world information; a digitizer coupled to the sensor to digitize the real world information into digitized information; a signal processor coupled to the digitizer to process the digitized information into an image; a discriminator coupled to the signal processor to determine, based at least in part on the image, whether the real world information comprises an anomaly, where the discriminator is trained via a generative adversarial network; and a controller coupled to the discriminator.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a divisional of U.S. patent application Ser.No. 17/089,193, filed on Nov. 4, 2020, the content of which is herebyincorporated by reference.

BACKGROUND

Anomalies are patterns in data that do not conform to a well-definednotion of normal behavior. Deep neural networks can be trained to detectanomalies in data patterns. However training such a network requires alarge amount of data. As an example, a training set may require on theorder of thousands of different samples. Abnormal (positive) cases aremuch rarer than normal (negative) cases, therefore data tends to beheavily skewed towards negative cases. Moreover this large amount ofdata needs to be labeled by human experts. It is time consuming andexpensive to collect large amounts of labeled data that isrepresentative of positive and negative real-world examples.

SUMMARY OF THE INVENTION

In one aspect, an apparatus includes: a sensor to sense real worldinformation; a digitizer coupled to the sensor to digitize the realworld information into digitized information; a signal processor coupledto the digitizer to process the digitized information into an image; adiscriminator coupled to the signal processor to determine, based atleast in part on the image, whether the real world information comprisesan anomaly, where the discriminator is trained via a generativeadversarial network; and a controller coupled to the discriminator. Inresponse to an indication of the anomaly from the discriminator, thecontroller is to trigger an action.

In an example, the image may be a spectrogram. The discriminator may bea binary classifier to output a probability value, where when theprobability value exceeds a threshold the anomaly is detected and whenthe probability value does not exceed the threshold no anomaly isdetected. The discriminator can be trained with samples of a positivecondition comprising the anomaly, the samples of the anomaly comprisingsynthetic samples generated by a generator of the generative adversarialnetwork and real samples of the anomaly. The trained discriminator is todetermine whether the real world information comprises the positivecondition comprising at least one of a glass break sound, a gunshotsound, a baby cry, a falling body, and an emergency siren. In anexample, the generator is to generate the synthetic samples including atleast some synthetic glass break sounds and at least some syntheticgunshot sounds. The apparatus may further include a non-volatile storagecomprising a plurality of binary classifiers, each associated with agiven anomaly. The discriminator may output a probability metriccomprising the indication of the anomaly. The controller may receive theprobability metric, and in response to the probability metric exceedinga threshold, trigger the action comprising a wireless communication ofan alert to a remote location.

In another aspect, a method includes: generating, in a generator of aGAN, a first plurality of random vectors of a latent space; generating,in the generator, a first plurality of random images using the firstplurality of random vectors; forming a training set including a mixtureof the first plurality of random images and a first plurality of realimages comprising one or more anomalous conditions; training adiscriminator of the GAN with the training set; and storing the traineddiscriminator in a non-transitory storage medium.

In an example, the method further comprises determining whether the GANis sufficiently trained. The method may further comprise determiningwhether the GAN is sufficiently trained based at least in part on afirst loss function associated with the discriminator and a second lossfunction associated with the GAN. The method further may include:generating, in the generator, a second plurality of random vectors ofthe latent space; generating, in the generator, a second plurality ofrandom images using the second plurality of random vectors, andidentifying the second plurality of random images as real images; andproviding the second plurality of random images with the identificationas the real images to the discriminator and training the generator basedat least in part on discriminator decisions for the second plurality ofrandom images.

In an example, the method further may include locking a plurality ofweights of the discriminator prior to providing the second plurality ofrandom images to the discriminator. The method also may compriseobtaining the first plurality of real images from a plurality ofanomalous real world samples comprising audio information of the one ormore anomalous conditions. The trained discriminator may be sent to oneor more end node devices to enable the one or more end node devices toidentify the one or more anomalous conditions in real world informationreceived in the one or more end node devices.

In another aspect, a system comprises a processor and a non-transitorystorage medium coupled to the processor. The non-transitory storagemedium may include instructions that when executed cause the processorto: receive, in a generator of a GAN, a first plurality of randomvectors of a latent space; generate, in the generator, a first pluralityof random images using the first plurality of random vectors; form atraining set including a mixture of the first plurality of random imagesand a first plurality of real images comprising one or more anomalousconditions; train a discriminator of the GAN with the training set; andstore the trained discriminator in a non-volatile storage.

In an example, the non-transitory storage medium further comprisesinstructions that when executed cause the processor to determine whetherthe GAN is sufficiently trained. The non-transitory storage mediumfurther may include instructions that when executed cause the processorto determine whether the GAN is sufficiently trained based at least inpart on a first loss function associated with the discriminator and asecond loss function associated with the GAN. The non-transitory storagemedium further may include instructions that when executed cause theprocessor to: lock a plurality of weights of the discriminator; receive,in the generator, a second plurality of random vectors of the latentspace; generate, in the generator, a second plurality of random imagesusing the second plurality of random vectors, and identify the secondplurality of random images as real images; and provide the secondplurality of random images with the identification as the real images tothe discriminator and train the generator based at least in part ondiscriminator decisions for the second plurality of random images.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an end node device in accordance with anembodiment.

FIG. 2 is a block diagram of a GAN in accordance with an embodiment.

FIGS. 3A and 3B are flow diagrams of a method in accordance with anembodiment.

FIG. 4 is a flow diagram of a method in accordance with an embodiment.

FIG. 5 is a block diagram of a representative device which may beincorporated into a wireless network as a node in accordance with anembodiment.

FIG. 6 is a block diagram of a representative wireless automationnetwork in accordance with an embodiment.

FIG. 7 is a block diagram of a configuration environment in accordancewith an embodiment.

DETAILED DESCRIPTION

In various embodiments, anomaly detection may be performed using adiscriminator trained in a Generative Adversarial Network (GAN). In thisGAN, either normal behavior or abnormal behavior is modeled using anadversarial training process. During inference, the discriminatornetwork is used to detect whether new data conforms to the trainedbehavior. Since only one type of behavior is used in training no labelsare required, such that an unsupervised classifier results. In somecases it is practical to train on normal behavior (for exampleaccelerometer data on normally operating electric motors) and in othercases it is more practical to train on abnormal behavior (for examplethe sound of glass breaking).

With an embodiment, a resulting trained discriminator network is lesscumbersome to deploy in the field than other networks used for anomalydetection. For example it is less complex than an autoencoder, whichrequires an encoder network and a decoder network. This discriminatormay also be less complex than a bidirectional GAN, as the discriminatoris based on a simple GAN architecture having a generator and adiscriminator. Only the discriminator is used during inference, wherethe discriminator can be trained on negative examples (normal behavior)or positive examples (abnormal behavior).

Referring now to FIG. 1 , shown is a block diagram of an end node devicein accordance with an embodiment. As shown in FIG. 1 , device 100 may beany type of end node device such as an Internet of Things (IoT) device.In some cases, device 100 may be a sensor, actuator, or any other typeof small wireless enabled device. In the high level shown in FIG. 1 ,device 100 includes an input sensor 110. Understand that in differentimplementations multiple sensors may be present, each to sense adifferent type of real world information such as environmentalcondition, image information, speech or other audio information or soforth. Input sensor 110 thus may include one or more microphones andpossibly other sensors such as image sensors, video sensors, thermalsensors, accelerometers, passive infrared sensors and so forth.

In turn, input sensor 110 is coupled to a digitizer 120, e.g., ananalog-to-digital converter (ADC), which digitizes the information andprovides it to a signal processor 130, which may perform various signalprocessing, such as filtering, amplification or so forth. Note that insome implementations, at least some of this signal processing may beperformed in an analog domain prior to digitizer 120. In any event, thedigitized processed information is provided to a processor 140, whichmay be a main processor of system of device 100, such as amicrocontroller, processing core or other general-purpose processingcircuitry or other such controller.

Relevant to the discussion herein, processor 140 may perform additionalsignal processing on this information, including converting thedigitized information into an image such as a spectrogram. In turn,processor 140 is coupled to a discriminator 150 which in some cases maybe implemented as a dedicated hardware circuit. In other cases,discriminator 150 is implemented in firmware and/or software. Indifferent embodiments, discriminator 150 can be implemented as a binaryclassifier to classify an input. Here in the context of a spectrogram,the input can be classified as one of two classes. In someimplementations, rather than a strict binary output, e.g., positive ornegative with respect to a class of interest, discriminator 150 mayoutput a confidence value, e.g., a percentage of likelihood as towhether the received input (e.g., spectrogram) is of the relevant class.As will be described herein, discriminator 150 may be trained in a GANsuch that a highly trained binary classifier is realized. Morespecifically, this binary classifier is trained to distinguish whethernew data conforms to the behavior it has been trained for (normal orabnormal). Note that in some cases at least two of signal processor 130,processor 140 and discriminator 150 may be implemented together.

In some cases, processor 140 may be configured to trigger some actionbased on the classification decision from discriminator 150. To this endprocessor 140 may include a control circuit 145 configured to trigger anaction based on a classification decision received from discriminator150. For example, in the context where an incoming sample is identifiedas an anomalous event, e.g., a gunshot, glass break or so forth,processor 140 may cause the triggering of an alarm condition. This alarmcondition may cause device 100 to take some action. In other cases, thisalarm condition may be communicated, e.g., wirelessly from end nodedevice 100 to a main controller of a system such as a security system orso forth.

In the case where an identification of an anomalous event is to becommunicated to a remote destination, processor 140 may receive thedecision from discriminator 150 and provide it to a wireless circuit170, which may wirelessly transmit the compressed information via anantenna 180. Understand that antenna 180 also may be configured toreceive incoming information, such as command information from thecloud-based destination to enable device 100 to perform some action inresponse to the command information received. Although shown at thishigh level in the embodiment of FIG. 1 , many variations andalternatives are possible. For example in other cases a device 100 maycommunicate via a wired connection.

Referring now to FIG. 2 , shown is a block diagram of a GAN inaccordance with an embodiment. In an embodiment, GAN 200 may beimplemented in one or more server computers such as cloud-based servers.As shown in FIG. 2 , GAN 200 includes a generator 210 and adiscriminator 230. With the arrangement in FIG. 2 , generator 210 maygenerate images. More specifically, generator 210 takes as input arandom vector 205 (vector in latent space) and generates a syntheticimage that resembles a real data pattern. These sample vectors in thelatent space may be obtained using a Gaussian distribution (vs. auniform distribution). These synthetic (e.g., random) or so-called“fake” images are based, at least in part, on random vectors 205, whichmay be obtained from a latent space. Accordingly, generator 210 outputssynthetic images (representative image 215 is shown). Next, suchsynthetic images may be combined with a training set formed of realimages (at block 220). This combination of images including both realand synthetic images is provided to discriminator 230.

In embodiments, discriminator 230 is a binary classifier that is trainedto classify input images as either real or fake. More specifically,discriminator 230 may be configured to take an input image (real orsynthetic) and predict whether the image came from a training set (realimage) or was created by generator 210. Discriminator 230 may beconfigured as a given type of neural network having a plurality oflayers, which can be implemented with convolutional layers or fullyconnected layers. In an embodiment, discriminator 230 is trained todistinguish between fake samples (i.e., generated) or real samples (fromthe training data set). The discriminator is a simple binary classifierthat may be configured to output an estimated probability of its inputbelonging to the training data set. As seen, these classificationdecisions that are output from discriminator 230 are provided astraining feedback to generator 210. As such, generator 210 operates suchthat it is trained to fool discriminator 230, evolving towardsgenerating increasingly realistic images as training proceeds. Note thatthis training of discriminator 230 may be performed offline. In anexample, discriminator training may be done in, e.g., 10's of minutes inone embodiment. Understand while shown at this high level in theembodiment of FIG. 2 , a GAN may be implemented in other manners inother implementations.

Referring now to FIGS. 3A and 3B, shown is a flow diagram of a method inaccordance with an embodiment. More specifically, these flow diagramsillustrate a method for training a discriminator and generator of a GAN.In an embodiment, this training method may be performed by one or morecomputing systems, such as at least one cloud-based server. Of course aGAN may be implemented in other computing systems, which may equallyperform the method herein.

As illustrated, method 300 begins by generating a plurality of randomvectors in a latent space (block 305). Although embodiments are notlimited in this regard, the number of random vectors may be in thethousands, in one example. From these random vectors, a plurality ofrandom images may be generated (block 310). In an embodiment, agenerator of the GAN may generate these random images. Note that whileimages are generated, these generated fake samples may be intended toreplicate a given audio sample, such as the sound of breaking glass, agunshot, a baby cry, a falling body, an emergency siren or so forth. Assuch, the random vectors are processed into image information, e.g., inthe form of a spectrogram.

Next at block 315 these random images may be mixed with real images.Understand that the real images may be obtained from a training set fora given characteristic for which a discriminator is desired to betrained on. These real images may be image information generated fromreal world information, in this case real world audio samples, e.g., thesound of breaking glass, gunshot, a baby cry, a falling body, anemergency siren or any other real world input to be classified by adiscriminator. As other examples, a discriminator may be configured todetect other environmental conditions such as image information, e.g.,detecting presence of a person, a person having a gun or otherrestricted item such as detecting a weapon in an X-ray machine,detecting tumors in a magnetic resonance image (MRI), vibrationinformation or so forth. Other examples may include presence detectionusing a passive infrared (PIR) detector.

To obtain real images from this real world information, a signalprocessor that receives the audio samples may generate correspondingspectrograms. In different embodiments, different numbers of real andsynthetic images may be mixed together. However, for effective training,approximately equal numbers of real and synthetic images may be used inan embodiment.

Still referring to FIG. 3A, next at block 320 the discriminator of theGAN may be trained. More specifically, the mixture of real and randomimages may be used to train the discriminator. The real samples (fromthe training dataset) are labeled as “real” and the fake samples(generated by the generator) are labeled as “fake”. In the context inwhich the discriminator is a binary classifier, as a matter ofconvention, the real images may be labeled with a value of logic 0 toindicate a real image and in turn, the random images may be labeled witha value of logic 1 to indicate a fake image. Note that these operationsfrom blocks 305-320 may be repeated several times (epochs).

Next at diamond 325 it is determined whether the GAN has beensufficiently trained. In one embodiment, this determination may be basedon analysis of at least one loss function for the discriminator and atleast one loss function for the GAN. If it is determined that the GANhas been sufficiently trained, e.g., if the loss functions are below oneor more thresholds, control passes to block 330. In an embodiment, theloss function for the discriminator is a binary cross-entropy. The otherloss function is an adversarial loss of the GAN which is also a binarycross-entropy.

At block 330, the trained discriminator may be provided to a device.Understand that this device may be an end node device such as a givenintegrated circuit, IoT device or so forth. The discriminator may beprovided to the device in different ways in different embodiments. Forexample, in cases in which the trained discriminator is generated priorto manufacture of an IC, firmware including this discriminator may beincluded within the IC as code stored in a non-volatile storage. Inother cases, the discriminator may be provided by way of programming anIC, IoT or other such device, either directly or by storing code of thediscriminator in a non-transitory storage medium that is accessible tothe device. Understand of course that prior to this block 330, thetrained discriminator may be stored in one or more non-volatile storagemedia of the server itself so that it can be maintained and provided tothe various devices. Also understand that based on further testing,field reports or so forth, it is possible that the discriminator may beupdated and/or additionally trained. After such additional training, anupdated discriminator may be provided by way of patch code or as newcode to take the place of existing discriminators by an in-field update.

Still with reference to FIG. 3A, instead if it is determined at diamond325 that the GAN is not sufficiently trained, control passes to block350 (of FIG. 3B). As illustrated here, a second plurality of randomvectors may be generated in a latent space. The same number of randomvectors generated as discussed above can be generated. Next at block355, a second plurality of images, e.g., additional synthetic (e.g.,random) images, may be generated using the second random vectors. Notethat these second plurality of images are fake images. Yet at block 360these second plurality of images may be identified as a real images, byapplying a real-valued label to these images. Continuing with theabove-described convention, these images may be labeled with a logic 0value.

Note that training occurs in a staggered fashion, with iterationsbetween discriminator training and generator training. To enablegenerator training to be performed using these images, first at block365 the discriminator weights may be locked. Next at block 370 thesesecond plurality of images are sent to the discriminator. Understandthat even though the discriminator receives these images with a labelthat indicates that they are real, based on its earlier training thediscriminator may output decisions to classify the images as anomalousor not anomalous. At block 375, the discriminator decisions are used tocompute an adversarial loss function for the GAN network (i.e., the lossof the gan(x)=discriminator(generator(x)). The generator may performtraining (at block 380) based on the adversarial loss. In the training,the generator may adapt weights in a direction that makes thediscriminator more likely to classify a synthetic image as real, usingthe GAN or adversarial loss derived from the GAN outputs.

Embodiments may perform certain optimizations in training a GAN inaccordance with an embodiment. To this end, it is noted that sparsegradients may hinder GAN training. According, max pooling may bereplaced with a strided convolution. Also a leaky rectified linear unit(ReLu) may be used, which allows small negative values. In addition,randomness may be introduced during training. Further, dropout may beused in the discriminator. Further to improve training, random noise maybe added to labels, and a tanh activation may be used in a last layer ofthe generator instead of a sigmoid. Of course, additional or differentoptimizations may be used in other embodiments.

Still with reference to FIGS. 3A and 3B, control passes back to block305, discussed above, where additional discriminator training may occur.Such operation may proceed iteratively until it is determined at diamond325 that the GAN is sufficiently trained, as described above. Completionof training may be achieved based at least in part on the discriminatorand adversarial loss functions, to obtain a discriminator trained todetect anomalies. While shown at this high level in the embodiment ofFIGS. 3A, 3B variations and alternatives are possible. Furthermore,understand that additional operations in performing training may occur.For example, some amount of noise may be added to the labels to improvetraining.

Table 1 below shows pseudo-code of an example training of a GAN inaccordance with an embodiment.

TABLE 1 0 start = 0 1 for step in range(iterations): 2random)_latent_vectors = np.random.normal(size=(batch_size, latent_dim))3 generated_images = generator.predict(random_latent_vectors) 4 stop =start + batch_size 5 real images = x_train[start: stop] 6combined_images = np.concatenate([generated_images, real_images]) 7labels = np.concatenate([np.ones((batch_size, 1)), np.zeros((batch size,1))]) 8 labels += 0.05 * np.random.random(labels.shape) 9 d_loss =discriminator.train_on_batch(combined_images, labels) 10random_latent_vectors = np.random.normal(size=(batch_size, latent dim))11 misleading targets = np.zeros((batch_size, 1)) 12 a_loss =gan.train_on_batch(random_latent_vectors, misleading_targets) 13 start+= batch_size

Note that in the above pseudo-code, line 9 establishes the discriminatorloss as a binary cross-entropy equal to a function of the predictedoutput probability of the discriminator (i.e., cross-entropy=f(truelabel=“fake/real”). And line 12 establishes the adversarial loss as abinary cross-entropy equal to a function of the predicted outputprobability of the GAN (i.e., f(true label=“always real”). Of course,while shown with this example code, understand that different trainingtechniques can be used in other embodiments.

Referring now to FIG. 4 , shown is a flow diagram of a method inaccordance with an embodiment. More specifically, method 400 is a methodperformed by an end node device to receive incoming real worldinformation, and process and classify it using a trained discriminatorin accordance with an embodiment. While for illustration FIG. 4 is inthe context of the real world information being audio information,understand embodiments are not limited in this regard and in otherimplementations, the real world information may be of a wide variety ofdifferent information types.

As illustrated, method 400 begins by receiving audio samples in the endnode device (block 410). For example, the end node device may include amicrophone or other sensor to detect audio input, such as environmentalnoise present in a given environment, which can of course include a widevariety of different sounds. Note that in other cases, the input may bean input received from an image sensor. At block 420 these audio samplesmay be digitized, e.g., after certain analog front end processing suchas filtering, signal conditioning and so forth. Next at block 430 thedigitized audio samples may be processed. In embodiments, the samplesmay be processed into a spectrogram. Of course other digitalrepresentations of the audio sample may be generated in otherimplementations such as may be appropriate for other types of inputinformation. For example, in another case a single fast Fouriertransform (FFT) may be used for a time stationary signal.

Still with reference to FIG. 4 , control next passes to block 440 wherethe spectrogram may be classified. More specifically, embodiments hereina discriminator may classify the spectrogram as one of two binaryclasses, e.g. positive or negative, normal or abnormal, expected or notexpected. Assume an example of environmental sound, in which thediscriminator is training for an anomalous event such as a glass breakor a gunshot sound. In this example, at block 440 the discriminator mayclassify the spectrogram as an anomalous event. To this end, thediscriminator may communicate an indication as a binary value for arepresentative classification and, in some cases, a confidence level ofthe classification. In some cases, the discriminator may output aprobability value. And, when the probability value exceeds a threshold,the anomaly is detected (and vice versa).

Then it is determined (e.g., in a processor coupled to thediscriminator) if the classification decision is an anomalous event(diamond 450). In the instance of a determination of the anomalousevent, at block 460, a processor may trigger an action in response tothis anomalous event indication. For example, the processor may beconfigured with logic to identify a particular type of anomalous event,e.g., based on the indication of the discriminator and the anomalousevent indication and perform a given action. This logic may include atable including multiple entries. Each entry may be associated with aparticular discriminator (in examples where there is more than onetrained discriminator within an end node device, with each discriminatortrained in a GAN with different data), the classification or confidencelevel (and possibly a threshold level for which the confidence level isto exceed) and the indicated action.

As one simple example, the indicated action may be for the processor totrigger an alert condition, e.g., sent in a message to a centralcontroller of a given network. In different examples, this centralcontroller may be in a local network such as a mesh network with the endnode device. In other cases, the end node device may communicate thealert condition to a cloud-based destination, e.g., a remote server forfurther handling. Understand while shown at this high level in theembodiment of FIG. 4 , many variations and alternatives are possible.

Embodiments may be implemented in many different types of end nodedevices. Referring now to FIG. 5 , shown is a block diagram of arepresentative device 500 which may be incorporated into a wirelessnetwork as a node. And as described herein, device 500 may sense anddetect an anomalous condition, e.g., based on processed spectrograms andcommunicate indication of the condition to a remote server, and in turnreceive commands from the remote server. In the embodiment shown in FIG.5 , device 500 may be a sensor, actuator, controller or other devicethat can be used in a variety of use cases in a wireless controlnetwork, including sensing, metering, monitoring, embedded applications,communications applications and so forth.

In the embodiment shown, device 500 includes a memory system 510 whichin an embodiment may include a non-volatile memory such as a flashmemory and volatile storage, such as RAM. In an embodiment, thisnon-volatile memory may be implemented as a non-transitory storagemedium that can store instructions and data. Such non-volatile memorymay store code and data (e.g., trained parameters) for one or morediscriminators, as described herein, and may also store code performingmethods including the method of FIG. 4 .

Memory system 510 couples via a bus 550 to a digital core 520, which mayinclude one or more cores and/or microcontrollers that act as a mainprocessing unit of the device. As shown, digital core 520 includes adiscriminator 525 which may make binary classifications of spectrograms,as described herein. As further shown, digital core 520 may couple toclock generators 530 which may provide one or more phase locked loops orother clock generation circuitry to generate various clocks for use bycircuitry of the device.

As further illustrated, device 500 further includes power circuitry 540,which may include one or more voltage regulators. Additional circuitrymay optionally be present depending on particular implementation toprovide various functionality and interaction with external devices.Such circuitry may include interface circuitry 560 which may provideinterface with various off-chip devices, sensor circuitry 570 which mayinclude various on-chip sensors including digital and analog sensors tosense desired signals, such as speech inputs, image inputs or so forth.

In addition as shown in FIG. 5 , transceiver circuitry 580 may beprovided to enable transmission and receipt of wireless signals, e.g.,according to one or more local area wireless communication schemes, suchas Zigbee, Bluetooth, Z-Wave, Thread or so forth. Understand while shownwith this high level view, many variations and alternatives arepossible.

Referring now to FIG. 6 , shown is a block diagram of a representativewireless automation network in accordance with an embodiment. As shownin FIG. 6 , network 600 may be implemented as a mesh network in whichvarious nodes 610 (610 _(0-n)) communicate with each other and also maycommunicate messages along from a particular source node to a givendestination node. In embodiments herein, one or more nodes 610 mayinclude classifiers trained using a GAN. Such message-basedcommunication also may be realized between various nodes 610 and anetwork controller 620.

Understand while shown at a very high level in FIG. 6 , network 600 maytake many forms other than a mesh network. For example, in other cases astar network, a vector-to-vector network or other network topologies maybe possible. Furthermore, while generic nodes 610 are shown for ease ofillustration, understand that many different types of nodes may bepresent in a particular application. For example, in the context of ahome automation system, nodes 610 may be of many different types. Asexamples, nodes 610 may include sensors, lighting components, door locksor other actuators, HVAC components, security components, windowcoverings, and possibly entertainment components, or other interfacedevices to enable control of such components via network 600. Of coursein different implementations additional or different types of nodes maybe present.

In addition, different nodes 610 may communicate according to differentwireless communication protocols. As examples, representativecommunication protocols may include Bluetooth, Zigbee, Z-Wave, andThread, among other possible wireless communication protocols. In somecases, certain nodes may be capable of communicating according tomultiple communication protocols, while other nodes only may be capableof communicating by a given one of the protocols. Within network 600,certain nodes 610 may communicate with other nodes of the samecommunication protocol, either for providing direct messagecommunication or for realizing mesh-based communications with networkcontroller 620 or other components. In other instances, e.g., forcertain Bluetooth devices, communications may be directly between agiven node 610 and network controller 620.

As such in the embodiment of FIG. 6 , network controller 620 may beimplemented as a multi-protocol universal gateway. Network controller620 may be the primary controller within the network and may beconfigured to perform operations to set up the network and enablecommunication between different nodes and update such set up as devicesenter and leave network 600.

In addition, network controller 620 further may be an interface tointeract with remote devices such as cloud-based devices. To this end,network controller 620 further may communicate, e.g., via the Internetwith a remote cloud server 640. Remote cloud server 640 may includeprocessors, memory and non-transitory storage media, which may be usedto generate and train a discriminator and perform the other operationsdescribed herein. As also shown, one or more user interfaces 650 thatcan be used to interact with network 600 may be located remotely and maycommunicate with network controller 620 via the Internet 630. Asexamples, such user interfaces 650 may be implemented within a mobiledevice such as a smartphone, tablet computer or so forth of a userauthorized to access network 600. For example, the user may be ahomeowner of a home in which wireless network 600 is implemented as ahome automation network. In other cases, the user may be an authorizedemployee such as an IT individual, a maintenance individual or so forthwho uses remote user interface 650 to interact with network 600, e.g.,in the context of a building automation network. Understand that manyother types of automation networks, such as an industrial automationnetwork, a smart city network, agricultural crop/livestock monitoringnetwork, environmental monitoring network, store shelf label network,asset tracking network, or health monitoring network, among others alsomay leverage embodiments as described herein.

Referring now to FIG. 7 , shown is a block diagram of a configurationenvironment in accordance with an embodiment. As shown in FIG. 7 ,configuration environment 700 includes a plurality of computing devices,including devices 705, 710. Computing devices 705, 710 may be any typeof computer systems, and in particular may be cloud-based servers.Understand that a skilled worker such as a chip designer may usecomputing device 705, which implements a GAN, to create a discriminatornetwork using an embodiment as described herein. This discriminatornetwork may then be provided to computing device 710 which may be of amanufacturing facility, to be used for incorporation of thediscriminator network into wireless devices.

Computing devices 705, 710 couple via an interconnect 708 (wired orwireless) and may include various circuitry including one or moreprocessors, communication buses, memory, display and other input/outputcircuitry, including a user input mechanism such as a touch panel,keypad or so forth and other circuitry. Understand that with embodimentsherein, an included memory and/or mass storage device (generallyillustrated at items 706, 715) may include instructions stored in anon-transitory storage medium for execution to create a traineddiscriminator using a GAN.

After the discriminator network is finalized and stored within computingdevice 710, it may be provided to a wireless device 750 for storage inan included non-volatile memory 755. In different implementations, thisdiscriminator may be provided to wireless device 750 via a wired orwireless interconnect 740. This discriminator can also be updatedwirelessly. For example, wireless device 750 can receive an update overthe air and the rewrite non-volatile memory 750 with an updateddiscriminator (e.g., with different weights). In other cases, adiscriminator network can be included into image burned into wirelesschips at production time or later, with multiple chips being imaged atonce in a factory setting. Understand while shown at this high level inthe embodiment of FIG. 7 , many variations and alternatives arepossible.

While the present invention has been described with respect to a limitednumber of embodiments, those skilled in the art will appreciate numerousmodifications and variations therefrom. It is intended that the appendedclaims cover all such modifications and variations as fall within thetrue spirit and scope of this present invention.

What is claimed is:
 1. An apparatus comprising: a sensor to sense realworld information; a digitizer coupled to the sensor to digitize thereal world information into digitized information; a signal processorcoupled to the digitizer to process the digitized information into animage; a discriminator comprising a binary classifier coupled to thesignal processor, the discriminator to determine, based at least in parton the image, whether the real world information comprises an anomaly,wherein the discriminator is trained via a generative adversarialnetwork with samples of a positive condition comprising the anomaly, thesamples of the anomaly comprising synthetic samples generated by agenerator of the generative adversarial network and real samples of theanomaly, wherein the binary classifier is to output a probability value,wherein when the probability value exceeds a threshold the anomaly isdetected and when the probability value does not exceed the threshold noanomaly is detected; and a controller coupled to the discriminator,wherein in response to an indication of the anomaly from thediscriminator, the controller is to trigger an action.
 2. The apparatusof claim 1, wherein the image comprises a spectrogram.
 3. The apparatusof claim 1, wherein the trained discriminator is to determine whetherthe real world information comprises the positive condition comprisingat least one of a glass break sound, a gunshot sound, a baby cry, afalling body, or an emergency siren.
 4. The apparatus of claim 3,wherein the generator is to generate the synthetic samples including atleast some synthetic glass break sounds or at least some syntheticgunshot sounds.
 5. The apparatus of claim 1, further comprising anon-volatile storage comprising a plurality of binary classifiers, eachassociated with a given anomaly.
 6. The apparatus of claim 1, whereinthe discriminator is to output a probability metric comprising theindication of the anomaly.
 7. The apparatus of claim 6, wherein thecontroller is to receive the probability metric, and in response to theprobability metric exceeding a threshold, the controller is to triggerthe action comprising a wireless communication of an alert to a remotelocation.
 8. The apparatus of claim 7, further comprising a wirelesscircuit to send the wireless communication of the alert to the remotelocation and receive a command from the remote location, wherein inresponse to the command the apparatus is to perform the action.
 9. Theapparatus of claim 1, wherein the real world information comprises audioinformation and the signal processor is to process the audio informationinto a spectrogram.
 10. The apparatus of claim 1, wherein the generativeadversarial network comprises the discriminator and the generator, theapparatus not including the generator.
 11. The apparatus of claim 10,wherein the generator and the discriminator are iteratively trainedprior to provision of the discriminator to the apparatus.
 12. A systemcomprising: a sensor to sense real world information; a digitizercoupled to the sensor to digitize the real world information intodigitized information; a signal processor coupled to the digitizer toprocess the digitized information into an image; a discriminator coupledto the signal processor, the discriminator to determine, based at leastin part on the image, whether the real world information comprises ananomaly, wherein the discriminator is trained via a generativeadversarial network having a generator that provides a plurality ofrandom images to the discriminator, the plurality of random imagesidentified to the discriminator as real images; an actuator to performan action; and a controller coupled to the discriminator, wherein inresponse to an indication of the anomaly from the discriminator, thecontroller is to cause the actuator to perform the action.
 13. Thesystem of claim 12, wherein the discriminator comprises a binaryclassifier, the binary classifier to output a probability value, whereinwhen the probability value exceeds a threshold the anomaly is detectedand when the probability value does not exceed the threshold no anomalyis detected.
 14. The system of claim 12, further comprising anon-volatile memory to store the discriminator, wherein the system is toreceive an updated discriminator for storage in the non-volatile memoryas an in-field update.
 15. A wireless device comprising: a sensor tosense real world information; a digitizer coupled to the sensor todigitize the real world information into digitized information; a signalprocessor coupled to the digitizer to process the digitized informationinto an image; a discriminator coupled to the signal processor, thediscriminator to determine, based at least in part on the image, whetherthe real world information comprises an anomaly, wherein thediscriminator is trained via a generative adversarial network locatedexternally to the wireless device, the wireless device not including thegenerative adversarial network, the generative adversarial network toprovide a plurality of random images to the discriminator, the pluralityof random images identified to the discriminator as real images; and awireless circuit coupled to the signal processor, wherein the wirelesscircuit is to communicate information with a remote device.
 16. Thewireless device of claim 15, wherein the discriminator is to output aprobability value, wherein when the probability value exceeds athreshold the anomaly is detected and when the probability value doesnot exceed the threshold no anomaly is detected.
 17. The wireless deviceof claim 15, wherein the discriminator is trained with samples of apositive condition comprising the anomaly, the samples of the positivecondition comprising the anomaly comprising synthetic samples generatedby the generative adversarial network and real samples of the anomaly.